Title: BEFA: bald eagle firefly algorithm enabled deep recurrent neural network-based food quality prediction using dairy products

Authors: Noothi Manisha; Madiraju Jagadeeshwar

Addresses: Department of Computer Science, Chaitanya Deemed to be University, Warangal Urban 506001, Telangana, India ' Department of Computer Science, Chaitanya Deemed to be University, Warangal Urban 506001, Telangana, India

Abstract: Food quality is defined as a collection of properties that differentiate each unit and influences acceptability degree of food by users or consumers. Owing to the nature of food, food quality prediction is highly significant after specific periods of storage or before use by consumers. However, the accuracy is the major problem in the existing methods. Hence, this paper presents a BEFA_DRNN approach for accurate food quality prediction using dairy products. Firstly, input data is fed to data normalisation phase, which is performed by min-max normalisation. Thereafter, normalised data is given to feature fusion phase that is conducted employing DNN with Canberra distance. Then, fused data is subjected to data augmentation stage, which is carried out utilising oversampling technique. Finally, food quality prediction is done wherein milk is graded employing DRNN. The training of DRNN is executed by proposed BEFA that is a combination of BES and FA. Additionally, BEFA_DRNN obtained maximum accuracy, TPR and TNR values of 93.6%, 92.5% and 90.7%.

Keywords: DNN; deep neural network; DRNN; deep recurrent neural network; BES; bald eagle search algorithm; firefly algorithm; min-max normalisation.

DOI: 10.1504/IJWMC.2024.142100

International Journal of Wireless and Mobile Computing, 2024 Vol.27 No.4, pp.320 - 334

Received: 05 May 2023
Accepted: 20 Apr 2024
Published online: 07 Oct 2024 *

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